Google Analytics User Analysis

Google Analytics Categorization

Categorizing ADC site page URL’s to more easily analyze user engagement.

Google Analytics Definitions

Page: The page shows the part of the URL after your domain name (path) when someone has viewed content on your website. For example, if someone views https://www.example.com/contact then /contact will be reported as the page inside the Behavior reports.
User: An individual visitor to the site (tracked using browser cookies)
Sessions: A single visit to the website, consisting of one or more pageviews, and any other interactions (The default session timeout is 30 minutes)
User % of Total: Users displayed as a percentage of the Total Users during the report period
Pageviews: The number of times users view a page that has the Google Analytics tracking code inserted. This covers all page views; so if a user refreshes the page, or navigates away from the page and returns, these are all counted as additional page views.
Unique Pageviews: The unique pageview is the count of all the times the page was viewed in an individual session as a single event. If a user viewed the page once in their visit or five times, the number of unique pageviews will be counted as just one
Entrances: Entrance represents the number of visits that started on a specific web page or group of web pages. I.e. the first page that someone views during a session
Bounce Rate: The Bounce Rate is Bounce measured in percentage. It represents the number of visits when users leave your site after just one page view, regardless of how long they stayed on that page. (Total Bounces divided by total visits)

Categorization Function

We will use the code and function below to categorize the Google Analytics dataset. The function takes messy character data within a dataframe and categorizes it based on a set of search string criteria. The inputs are the data frame, the column name of the messy data, a list of search strings, a list of category names (these have to be correlated), and you have the option of naming the new column.

It is important to note that the order of the search strings matters for strings that are repeats – i.e. “catalog” and “catalog/submit” will be written over so you must identify the longer string first (i.e. catalog/submit). Additionally, make sure the order of the categories list correlates with the order of the search strings.
Source: https://github.com/lenwood

Identify Search Strings and Category Names

# List of search strings -- note that the longer search strings are identified first 
search <- c("news", "portals", "about","catalogprofile", "catalogsubmit", "catalog", "training", "team", "home", "view", "submit", "profile")

# List of categories
categories <- c("News", "Portals", "About", "Summary", "Submit", "Cathome", "Training", "Team", "Home", "Dataset", "WhoMustSub", "Summary")

Create Categorization Function

# Quickly categorize a data frame with a column of messy character strings. 

# Replace "df" with your messy dataframe.

categorizeDF <- function(df, searchColName, searchList, catList, newColName="Category") {
  # create empty data frame to hold categories
  catDF <- data.frame(matrix(ncol=ncol(df), nrow=0))
  colnames(catDF) <- paste0(names(df))

  # add sequence so original order can be restored
  df$sequence <- seq(nrow(df))

  # iterate through the strings
  for (i in seq_along(searchList)) {
    rownames(df) <- NULL
    index <- grep(searchList[i], df[,which(colnames(df) == searchColName)], ignore.case=TRUE)
    tempDF <- df[index,]
    tempDF$newCol <- catList[i]
    catDF <- rbind(catDF, tempDF)
    df <- df[-index,]
  }

  # OTHER category for unmatched rows
  if (nrow(df) > 0) {
    df$newCol <- "OTHER"
    catDF <- rbind(catDF, df)
  }

  # return to the original order & remove the sequence data
  catDF <- catDF[order(catDF$sequence),]
  catDF$sequence <- NULL

  # remove row names
  rownames(catDF) <- NULL

  # set Category type to factor
  catDF$newCol <- as.factor(catDF$newCol)

  # rename the new column
  colnames(catDF)[which(colnames(catDF) == "newCol")] <- newColName
  catDF
}

Call Function and Categorize Data

# Replace "df" with messy dataframe

# Identify which column you want to categorize -- in our case with Google Analytics, we will be categorizing the "Page" column that contains messy URL strings. Additionally, you can name the new column that contains the categories (e.g. "Category").

sorted <- categorizeDF(df, "column name with messy data", search, categories, "new category column name")

Test Run of Categorization with Small Subset of Data

###### TEST DATASET ######


# Remove backslashes and other symbols from Page column (includes hyphens and periods). **** Not sure if this is necessary. Am trying to differentiate the single "/" as the ADC Homepage, and make it easier to identify search terms for the function below. 
test_users_clean <- top_30_users %>%
  mutate_all(funs(gsub("[[:punct:]]", "", .)))


# Rename home page as "home" in dataframe **NOTE that for this particular dataset the "Home" page is the top viewed page and so I put in [1]. If it is not the top viewed page you will need to determine which row the homepage is and put that row number in the brackets. *** Is there a better way to do this?? ***

test_users_clean$Page[1] <- "home"

### Categorize the page URLS in the Page column into larger categories using a function ###

## Create a list of search strings to sort through pages and a list of categories (these must be correlated) **Order matters for strings that are repeats -- i.e. "catalog" and "catalog/submit" will be written over so you must identify the longer string first (i.e. catalog/submit). 

# List of search strings
search <- c("news", "portals", "about","catalogprofile", "catalogsubmit", "catalog", "training", "team", "home", "view", "submit", "profile")

# List of categories
categories <- c("News", "Portals", "About", "Summary", "Submit", "Cathome", "Training", "Team", "Home", "Dataset", "WhoMustSub", "Summary")



## Create function [below] to categorize the messy "Page" column of the raw data frame. 
# This function takes looks at a data frame column of messy character (or factorial) data, and produces a new column of categorized data. The inputs are the data frame, the column name of the messy data, a list of search strings, a list of category names (these two have to be correlated), and you have the option of naming the new column.


# Function:
categorizeDF <- function(test_users_clean, searchColName, searchList, catList, newColName="Category") {
  # create empty data frame to hold categories
  catDF <- data.frame(matrix(ncol=ncol(test_users_clean), nrow=0))
  colnames(catDF) <- paste0(names(test_users_clean))

  # add sequence so original order can be restored
  test_users_clean$sequence <- seq(nrow(test_users_clean))

  # iterate through the strings
  for (i in seq_along(searchList)) {
    rownames(test_users_clean) <- NULL
    index <- grep(searchList[i], test_users_clean[,which(colnames(test_users_clean) == searchColName)], ignore.case=TRUE)
    tempDF <- test_users_clean[index,]
    tempDF$newCol <- catList[i]
    catDF <- rbind(catDF, tempDF)
    test_users_clean <- test_users_clean[-index,]
  }

  # OTHER category for unmatched rows
  if (nrow(test_users_clean) > 0) {
    test_users_clean$newCol <- "OTHER"
    catDF <- rbind(catDF, test_users_clean)
  }

  # return to the original order & remove the sequence data
  catDF <- catDF[order(catDF$sequence),]
  catDF$sequence <- NULL

  # remove row names
  rownames(catDF) <- NULL

  # set Category type to factor
  catDF$newCol <- as.factor(catDF$newCol)

  # rename the new column
  colnames(catDF)[which(colnames(catDF) == "newCol")] <- newColName
  catDF
}

# Call the function and create new data frame - using the raw data frame, the messy column you want to sort, the search and category lists, and name of the new column

sortedDF <- categorizeDF(test_users_clean, "Page", search, categories, "Category")

knitr::kable(sortedDF, format = "html")

Page	Users	Sessions	Users_._of_Total	Pageviews	Unique_Pageviews	Entrances	Bounce_Rate	Category
home	25436	42464	0440145	75951	55359	42443	0421957423	Home
catalog	4310	2280	0257363	12070	8734	2131	0247368421	Cathome
catalog	4130	416	0195397	33	26	19	0033653846	Cathome
data	3291	2306	0160785	19380	9507	2319	0273200347	OTHER
catalogdata	3114	3395	0139405	14923	7964	3298	0253608247	Cathome
about	2637	941	0123776	3942	3297	944	0582359192	About
team	1634	614	0110133	2554	2117	615	0684039088	Team
submit	1384	898	009936	3255	2395	901	0643652561	WhoMustSub
page0	1174	1580	0090577	5362	3813	1582	0158860759	OTHER
training	1166	892	0083537	2245	1639	892	0515695067	Training
publications	1120	431	0077705	1701	1326	432	0744779582	OTHER
share	1060	528	0072758	4532	2706	529	0357954545	OTHER
profile	989	232	0068477	1609	1338	238	061637931	Summary
qanda	912	214	0064713	1430	1181	214	0570093458	OTHER
january2019datasciencetrainingforarcticresearchers	903	1004	0061441	1359	1191	1004	0815737052	Training
datapage0	873	556	0058545	4704	2254	557	0303956835	OTHER
catalogprofile	799	193	0055914	1376	1121	188	0564766839	Summary
proposals	773	660	0053551	1187	1008	661	0762121212	OTHER
homehtm	735	800	0051402	936	817	800	056875	Home
support	729	121	0049463	1308	1004	122	058677686	OTHER
dataplans	685	384	0047672	932	827	384	0841145833	OTHER
2018datasciencetrainingforarcticresearchers	649	639	0046015	982	876	639	0723004695	Training
news201606datascienceopportunities	629	371	0044488	810	733	371	0851752022	News
upcomingdatasciencetrainingforarcticresearchers	599	612	0043066	857	767	612	0823529412	Training
catalogsubmit	582	302	0041746	1657	1196	304	0523178808	Submit
catalogportalspermafrost	548	651	0040505	874	722	650	0769585253	Portals
reconcilinghistoricalandcontemporarytrendsinterrestrialcarbonexchangeofthenorthernpermafrostzone	546	844	0039355	1189	995	844	0808056872	OTHER
viewdoi103334CDIAC00001V2017	522	562	0038272	769	619	562	0807829181	Dataset
catalogshare	521	399	0037263	1197	994	378	0483709273	Cathome
categorynews	512	197	0036317	822	672	197	0624365482	News

Visualizations for User Analysis

##  2016  2017  2018  2019  2020 
## 23772 32205 48337 31187 60330

## `summarise()` ungrouping output (override with `.groups` argument)

## $tm
##     Year         Category vSize vColor stdErr vColorValue level        x0
## 1   2016            About   366      3    368          NA     2 0.9307865
## 2   2016              API    11      3     13          NA     2 0.9809736
## 3   2016          Cathome  3022      3   3024          NA     2 0.9119412
## 4   2016        DataPlans    93      3     95          NA     2 0.9543366
## 5   2016 DataPreservation     3      3      5          NA     2 0.9982011
## 6   2016          Dataset 12211      3  12213          NA     2 0.8074770
## 7   2016          History    85      3     87          NA     2 0.9809736
## 8   2016             Home  3585      3   3587          NA     2 0.8074770
## 9   2016              KNB    11      3     13          NA     2 0.9934042
## 10  2016           MyData   178      3    180          NA     2 0.9307865
## 11  2016             <NA> 23772     25  23774          NA     1 0.8074770
## 12  2016             News   880      3    882          NA     2 0.8701616
## 13  2016            OTHER  1613      3   1615          NA     2 0.8074770
## 14  2016           Outage     1      3      3          NA     2 0.9967021
## 15  2016         Projects    38      3     40          NA     2 0.9809736
## 16  2016     Publications   170      3    172          NA     2 0.9776289
## 17  2016               QA   199      3    201          NA     2 0.9307865
## 18  2016       SearchTips     8      3     10          NA     2 0.9934042
## 19  2016          Summary   680      3    682          NA     2 0.8701616
## 20  2016          Support   131      3    133          NA     2 0.9543366
## 21  2016             Team   307      3    309          NA     2 0.9684271
## 22  2016         Training     1      3      3          NA     2 0.9983510
## 23  2016          Webinar     2      3      4          NA     2 0.9934042
## 24  2016       WhoMustSub   177      3    179          NA     2 0.9543366
## 25  2017            About   549      3    551          NA     2 0.8936335
## 26  2017              API    31      3     33          NA     2 0.9876467
## 27  2017          Cathome  1690      3   1692          NA     2 0.8248908
## 28  2017        DataPlans   106      3    108          NA     2 0.9869822
## 29  2017          Dataset 19535      3  19537          NA     2 0.5549019
## 30  2017          History    98      3    100          NA     2 0.9667187
## 31  2017             Home  4273      3   4275          NA     2 0.8248908
## 32  2017              KNB    46      3     48          NA     2 0.9667187
## 33  2017           MyData   184      3    186          NA     2 0.9426230
## 34  2017             <NA> 32205     24  32207          NA     1 0.5549019
## 35  2017             News   662      3    664          NA     2 0.8936335
## 36  2017            OTHER  2044      3   2046          NA     2 0.9433397
## 37  2017         Projects    38      3     40          NA     2 0.9876467
## 38  2017        Proposals   302      3    304          NA     2 0.9189513
## 39  2017     Publications   221      3    223          NA     2 0.9752820
## 40  2017               QA   178      3    180          NA     2 0.9426230
## 41  2017       SearchTips    14      3     16          NA     2 0.9876467
## 42  2017          Summary   804      3    806          NA     2 0.8248908
## 43  2017          Support   165      3    167          NA     2 0.9667187
## 44  2017             Team   323      3    325          NA     2 0.8936335
## 45  2017         Training   648      3    650          NA     2 0.9473851
## 46  2017          Webinar     2      3      4          NA     2 0.9984558
## 47  2017       WhoMustSub   292      3    294          NA     2 0.9426230
## 48  2018            About   627      3    629          NA     2 0.4837325
## 49  2018              API    21      3     23          NA     2 0.5383027
## 50  2018          Cathome  2454      3   2456          NA     2 0.3039637
## 51  2018        DataPlans   147      3    149          NA     2 0.5080145
## 52  2018          Dataset 26478      3  26480          NA     2 0.0000000
## 53  2018          History   105      3    107          NA     2 0.5383027
## 54  2018             Home  7559      3   7561          NA     2 0.3039637
## 55  2018              KNB     4      3      6          NA     2 0.5454167
## 56  2018           MyData   170      3    172          NA     2 0.5080145
## 57  2018             <NA> 48337     24  48339          NA     1 0.0000000
## 58  2018             News   450      3    452          NA     2 0.5251654
## 59  2018            OTHER  3828      3   3830          NA     2 0.4705433
## 60  2018         Projects    16      3     18          NA     2 0.5454167
## 61  2018        Proposals   403      3    405          NA     2 0.4837325
## 62  2018     Publications   211      3    213          NA     2 0.4837325
## 63  2018               QA   180      3    182          NA     2 0.4837325
## 64  2018       SearchTips     8      3     10          NA     2 0.5517402
## 65  2018           Submit   134      3    136          NA     2 0.5080145
## 66  2018          Summary  1324      3   1326          NA     2 0.4093999
## 67  2018          Support   150      3    152          NA     2 0.5329235
## 68  2018             Team   344      3    346          NA     2 0.5221278
## 69  2018         Training  1946      3   1948          NA     2 0.3039637
## 70  2018       WhoMustSub  1778      3   1780          NA     2 0.4093999
## 71  2019            About   588      3    590          NA     2 0.7034105
## 72  2019              API     2      3      4          NA     2 0.8038399
## 73  2019             Blog   108      3    110          NA     2 0.7746135
## 74  2019          Cathome  4522      3   4524          NA     2 0.5549019
## 75  2019        DataPlans   223      3    225          NA     2 0.7790748
## 76  2019          Dataset 14501      3  14503          NA     2 0.5549019
## 77  2019          History   103      3    105          NA     2 0.7914346
## 78  2019             Home  5289      3   5291          NA     2 0.5549019
## 79  2019              KNB     1      3      3          NA     2 0.8062646
## 80  2019             <NA> 31187     25  31189          NA     1 0.5549019
## 81  2019             News   346      3    348          NA     2 0.7034105
## 82  2019            OTHER  1893      3   1895          NA     2 0.7034105
## 83  2019          Portals   442      3    444          NA     2 0.7034105
## 84  2019         Projects    45      3     47          NA     2 0.7941411
## 85  2019        Proposals    82      3     84          NA     2 0.7746135
## 86  2019     Publications   245      3    247          NA     2 0.7478707
## 87  2019               QA   157      3    159          NA     2 0.7478707
## 88  2019       SearchTips     8      3     10          NA     2 0.7941411
## 89  2019           Submit   732      3    734          NA     2 0.7784573
## 90  2019          Summary   530      3    532          NA     2 0.7034105
## 91  2019          Support   127      3    129          NA     2 0.7478707
## 92  2019             Team   340      3    342          NA     2 0.7478707
## 93  2019         Training   577      3    579          NA     2 0.7559351
## 94  2019       WhoMustSub   326      3    328          NA     2 0.7783003
## 95  2020            About   615      3    617          NA     2 0.4478588
## 96  2020              API     2      3      4          NA     2 0.5475081
## 97  2020             Blog   701      3    703          NA     2 0.5056649
## 98  2020          Cathome  5783      3   5785          NA     2 0.4461653
## 99  2020        DataPlans   129      3    131          NA     2 0.5319809
## 100 2020          Dataset 36322      3  36324          NA     2 0.0000000
## 101 2020          History    84      3     86          NA     2 0.5319809
## 102 2020             Home  5961      3   5963          NA     2 0.3340817
## 103 2020              KNB     1      3      3          NA     2 0.5530535
## 104 2020           MyData     2      3      4          NA     2 0.5530535
## 105 2020             <NA> 60330     25  60332          NA     1 0.0000000
## 106 2020             News  1132      3   1134          NA     2 0.4478588
## 107 2020            OTHER  1814      3   1816          NA     2 0.3340817
## 108 2020          Portals  2815      3   2817          NA     2 0.3340817
## 109 2020         Projects    28      3     30          NA     2 0.5475081
## 110 2020     Publications   295      3    297          NA     2 0.5278539
## 111 2020               QA   217      3    219          NA     2 0.4940209
## 112 2020       SearchTips     7      3      9          NA     2 0.5475081
## 113 2020           Submit  1690      3   1692          NA     2 0.3929834
## 114 2020          Summary   823      3    825          NA     2 0.4478588
## 115 2020          Support   202      3    204          NA     2 0.4940209
## 116 2020             Team   398      3    400          NA     2 0.4478588
## 117 2020         Training   940      3    942          NA     2 0.5063399
## 118 2020       WhoMustSub   369      3    371          NA     2 0.4940209
##              y0           w           h   color
## 1   0.081746241 0.037640601 0.049652726 #035C47
## 2   0.000000000 0.012430589 0.004518763 #035C48
## 3   0.131398967 0.088058800 0.175242829 #035C49
## 4   0.000000000 0.026636977 0.017828572 #035C4B
## 5   0.003096774 0.001798861 0.008516129 #035C4C
## 6   0.306641796 0.192522995 0.323882274 #035C4D
## 7   0.020129033 0.019026412 0.022812904 #035C4F
## 8   0.131398967 0.104464195 0.175242829 #035C50
## 9   0.011612904 0.006595823 0.008516129 #035C51
## 10  0.000000000 0.023550065 0.038596368 #035C53
## 11  0.000000000 0.192522995 0.630524070 #146660
## 12  0.057276473 0.060624924 0.074122494 #035C54
## 13  0.000000000 0.062684617 0.131398967 #035C55
## 14  0.000000000 0.001648956 0.003096774 #035C57
## 15  0.004518763 0.012430589 0.015610271 #035C58
## 16  0.042941938 0.022371113 0.038804303 #035C59
## 17  0.038596368 0.023550065 0.043149873 #035C5B
## 18  0.003096774 0.004796962 0.008516129 #035C5C
## 19  0.000000000 0.060624924 0.057276473 #035A5C
## 20  0.017828572 0.026636977 0.025113365 #03595C
## 21  0.081746241 0.031572854 0.049652726 #03585C
## 22  0.000000000 0.001648956 0.003096774 #03565C
## 23  0.000000000 0.003297911 0.003096774 #03555C
## 24  0.042941938 0.023292276 0.038804303 #03545C
## 25  0.695671212 0.048989523 0.057225250 #007B5B
## 26  0.637137923 0.012353328 0.012814341 #007B5D
## 27  0.690247946 0.068742705 0.125538995 #007B5F
## 28  0.665660165 0.013017786 0.041580270 #007B61
## 29  0.630524070 0.269988846 0.369475930 #007B63
## 30  0.641748100 0.020927992 0.023912065 #007B65
## 31  0.815786941 0.118448902 0.184213059 #007B67
## 32  0.630524070 0.020927992 0.011224030 #007B69
## 33  0.668246481 0.024095676 0.038993953 #007B6B
## 34  0.630524070 0.445098069 0.369475930 #16897E
## 35  0.752896461 0.053751630 0.062890480 #007B6D
## 36  0.815786941 0.056660322 0.184213059 #007B6F
## 37  0.649952263 0.012353328 0.015707901 #007B70
## 38  0.630524070 0.023671737 0.065147142 #007B72
## 39  0.707240435 0.024717965 0.045656027 #007B74
## 40  0.630524070 0.024095676 0.037722411 #007B76
## 41  0.630524070 0.010809162 0.006613853 #007B78
## 42  0.630524070 0.068742705 0.059723877 #007B7A
## 43  0.665660165 0.020263534 0.041580270 #007A7B
## 44  0.630524070 0.025317785 0.065147142 #00787B
## 45  0.752896461 0.052614889 0.062890480 #00767B
## 46  0.630524070 0.001544166 0.006613853 #00757B
## 47  0.707240435 0.032659031 0.045656027 #00737B
## 48  0.135823689 0.041432915 0.077275284 #56E47A
## 49  0.000000000 0.007113948 0.015073954 #56E47C
## 50  0.094247864 0.105436232 0.118851110 #56E47F
## 51  0.022591773 0.030288171 0.024783512 #56E481
## 52  0.000000000 0.303963699 0.444817654 #56E483
## 53  0.015073954 0.016599211 0.032301330 #56E485
## 54  0.213098974 0.166579617 0.231718680 #56E488
## 55  0.000000000 0.009485263 0.002153422 #56E48A
## 56  0.047375285 0.024908922 0.034850784 #56E48C
## 57  0.000000000 0.554901931 0.444817654 #7AFDB1
## 58  0.135823689 0.029736542 0.077275284 #56E48E
## 59  0.213098974 0.084358615 0.231718680 #56E490
## 60  0.002153422 0.006323509 0.012920532 #56E493
## 61  0.082226069 0.038395302 0.053597621 #56E495
## 62  0.037853433 0.024282075 0.044372636 #56E497
## 63  0.000000000 0.024282075 0.037853433 #56E499
## 64  0.002153422 0.003161754 0.012920532 #56E49C
## 65  0.000000000 0.030288171 0.022591773 #56E49E
## 66  0.000000000 0.074332543 0.090955204 #56E4A0
## 67  0.047375285 0.021978460 0.034850784 #56E4A2
## 68  0.082226069 0.032774154 0.053597621 #56E4A5
## 69  0.000000000 0.105436232 0.094247864 #56E4A7
## 70  0.090955204 0.074332543 0.122143770 #56E4A9
## 71  0.151378004 0.052524551 0.057165438 #64D443
## 72  0.000000000 0.002424713 0.004211999 #61D443
## 73  0.021442904 0.016821137 0.032785889 #5FD443
## 74  0.000000000 0.148508573 0.155488256 #5DD443
## 75  0.054228793 0.028402158 0.040093325 #5BD443
## 76  0.337349685 0.252575074 0.293174385 #58D443
## 77  0.021442904 0.016042381 0.032785889 #56D443
## 78  0.155488256 0.148508573 0.181861429 #54D443
## 79  0.000000000 0.001212356 0.004211999 #52D443
## 80  0.000000000 0.252575074 0.630524070 #6DEC65
## 81  0.000000000 0.044460178 0.039739597 #50D443
## 82  0.208543442 0.075046814 0.128806243 #4DD443
## 83  0.039739597 0.044460178 0.050765613 #4BD443
## 84  0.004211999 0.013335920 0.017230905 #49D443
## 85  0.000000000 0.019527598 0.021442904 #47D443
## 86  0.054228793 0.031204165 0.040093325 #45D443
## 87  0.024250200 0.026742805 0.029978593 #43D443
## 88  0.000000000 0.009698851 0.004211999 #43D445
## 89  0.208543442 0.029019687 0.128806243 #43D447
## 90  0.090505210 0.044460178 0.060872794 #43D449
## 91  0.000000000 0.026742805 0.024250200 #43D44C
## 92  0.094322118 0.030429654 0.057055886 #43D44E
## 93  0.151378004 0.051541949 0.057165438 #43D450
## 94  0.094322118 0.029176669 0.057055886 #43D452
## 95  0.488844400 0.046162045 0.068031279 #132247
## 96  0.444817654 0.005545400 0.001841686 #132247
## 97  0.556875679 0.049237009 0.072701758 #132147
## 98  0.728421298 0.108736673 0.271578702 #132047
## 99  0.472442950 0.022920986 0.028739219 #131F47
## 100 0.444817654 0.334081683 0.555182346 #131E47
## 101 0.444817654 0.015527120 0.027625296 #131E47
## 102 0.728421298 0.112083574 0.271578702 #131D47
## 103 0.444817654 0.001848467 0.002762530 #131C47
## 104 0.447580184 0.001848467 0.005525059 #131B47
## 105 0.444817654 0.554901931 0.555182346 #1F254F
## 106 0.629577438 0.058481067 0.098843861 #131B47
## 107 0.444817654 0.058901763 0.157263360 #131A47
## 108 0.602081014 0.113777165 0.126340285 #131947
## 109 0.453105243 0.007393867 0.019337707 #131847
## 110 0.501182170 0.027048052 0.055693510 #131747
## 111 0.471991000 0.037960052 0.029191169 #131747
## 112 0.446659340 0.005545400 0.006445902 #131647
## 113 0.444817654 0.054875402 0.157263360 #131547
## 114 0.556875679 0.057806075 0.072701758 #131447
## 115 0.444817654 0.037960052 0.027173346 #131347
## 116 0.444817654 0.046162045 0.044026746 #141347
## 117 0.629577438 0.048562017 0.098843861 #151347
## 118 0.501182170 0.033832987 0.055693510 #151347
## 
## $type
## [1] "index"
## 
## $vSize
## [1] "Users"
## 
## $vColor
## [1] NA
## 
## $stdErr
## [1] "Users"
## 
## $algorithm
## [1] "pivotSize"
## 
## $vpCoorX
## [1] 0.02812148 0.97187852
## 
## $vpCoorY
## [1] 0.01968504 0.91031496
## 
## $aspRatio
## [1] 1.483512
## 
## $range
## [1] NA
## 
## $mapping
## [1] NA NA NA
## 
## $draw
## [1] TRUE